Papers › DeepCollide: Scalable Data-Driven High DoF Configuration Space Modeling using Implicit...
DeepCollide: Scalable Data-Driven High DoF Configuration Space Modeling using Implicit Neural Representations
Gabriel Guo, Judah Goldfeder, Aniv Ray, Tony Dear, Hod Lipson
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Collision detection is essential to virtually all robotics applications. However, traditional geometric collision detection methods generally require pre-existing workspace geometry representations; thus, they are unable to infer the collision detection function from sampled data when geometric information is unavailable. Learning-based approaches can overcome this limitation. Following this line of research, we present DeepCollide, an implicit neural representation method for approximating the collision detection function from sampled collision data. As shown by our theoretical analysis and empirical evidence, DeepCollide presents clear benefits over the state-of-the-art, as it relates to time cost scalability with respect to training data and DoF, as well as the ability to accurately express complex workspace geometries. We publicly release our code.
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